Recognition of vehicle country from license plate image based on Siamese network model with triplet loss function and negative sampling technique

The task of recognizing license plates from photographs or videos is extremely relevant nowadays. The development of transport links between cities and countries has led to the need to recognize vehicles from different regions. However, license numbers have their own characteristics for each country, such as differences in symbols, their position and font, which complicate recognition. A universal automatic recognition system must cope with these features. Our previous studies have proven the effectiveness of a model trained specifically for a target country, which for effective recognition requires an additional decision for choosing a country or region. The data used is a dataset of images of license plates mainly from countries that previously belonged to the CIS. We propose a vehicle country recognition model from license plate image based on siamese network model with triplet loss function and negative sampling technique. This model is significantly superior to a solution based on a convolutional network in terms of recognition accuracy, achieving 0.9651 value, as well as time spent on training.